The Pragmatic Roots of Context Export

@book{citeulike:1153316, abstract = {When modelling complex systems one can not include all the causal factors, but one has to settle for partial models. This is alright if the factors left out are either so constant that they can be ignored or one is able to recognise the circumstances when they will be such that the partial model applies. The transference of knowledge from the point of application to the point of learning utilises a combination of recognition and inference – a simple model of the important features is learnt and later situations where inferences can be drawn from the model are recognised. Context is an abstraction of the collection of background features that are later recognised. Different heuristics for recognition and model formulation will be effective for different learning tasks. Each of these will lead to a different type of context. Given this, there two ways of modelling context: one can either attempt to investigate the contexts that arise out of the heuristics that a particular agent actua lly applies or one can attempt to model context using the external source of regularity that the heuristics exploit. There are also two basic methodologies for the investigation of context: a top-down approach where one tries to lay down general, a priori principles and a bottom-up approach where one can try and find what sorts of context arise by experiment and simulation. A simulation is exhibited which is designed to illustrate the practicality of the bottom-up approach in elucidating the sorts of internal context that arise in an artificial agent which is attempting to learn simple models of a complex environment.}, author = {Edmonds, Bruce}, citeulike-article-id = {1153316}, citeulike-linkout-0 = {http://www.springerlink.com/content/cg26ep9m7ca500b4}, comment = {== Defines context as "the abstraction of those elements of the circumstances in which a model is learnt, that are not used explicitly in the production of an inference or prediction when the model is later applied, that allow the recognition of new circumstances where the model can be usefully applied." == Presents some considerations about modelling contexts * Internal (from the user point of view) / External (outside manifestations of individual's constructs) * Bottom-up / Top-down approaches to modelling context == Proposes a bottom-up internal model * Neural network with "switching" and "switched" edges: switching edges activate or disactivate switched edges. Thus a node can dis-activate the firing of edges, thus modelling context, i.e. some edges are transmitting only in certain context}, journal = {: Modeling and Using Context: Second International and Interdisciplinary Conference, CONTEXT'99, Trento, Italy, September 1999. Proceedings}, keywords = {context, l3s\_context\_workshop, model, pragmatic}, pages = {119+}, posted-at = {2007-09-22 16:05:06}, priority = {0}, title = {The Pragmatic Roots of Context}, url = {http://www.springerlink.com/content/cg26ep9m7ca500b4}, year = {1999} }

TY - BOOK ID - citeulike:1153316 L3 - citeulike-article-id:1153316 N2 - When modelling complex systems one can not include all the causal factors, but one has to settle for partial models. This is alright if the factors left out are either so constant that they can be ignored or one is able to recognise the circumstances when they will be such that the partial model applies. The transference of knowledge from the point of application to the point of learning utilises a combination of recognition and inference – a simple model of the important features is learnt and later situations where inferences can be drawn from the model are recognised. Context is an abstraction of the collection of background features that are later recognised. Different heuristics for recognition and model formulation will be effective for different learning tasks. Each of these will lead to a different type of context. Given this, there two ways of modelling context: one can either attempt to investigate the contexts that arise out of the heuristics that a particular agent actua lly applies or one can attempt to model context using the external source of regularity that the heuristics exploit. There are also two basic methodologies for the investigation of context: a top-down approach where one tries to lay down general, a priori principles and a bottom-up approach where one can try and find what sorts of context arise by experiment and simulation. A simulation is exhibited which is designed to illustrate the practicality of the bottom-up approach in elucidating the sorts of internal context that arise in an artificial agent which is attempting to learn simple models of a complex environment. JF - : Modeling and Using Context: Second International and Interdisciplinary Conference, CONTEXT'99, Trento, Italy, September 1999. Proceedings TI - The Pragmatic Roots of Context SP - 119 KW - context KW - l3s_context_workshop KW - model KW - pragmatic N1 - == Defines context as "the abstraction of those elements of the circumstances in which a model is learnt, that are not used explicitly in the production of an inference or prediction when the model is later applied, that allow the recognition of new circumstances where the model can be usefully applied." == Presents some considerations about modelling contexts * Internal (from the user point of view) / External (outside manifestations of individual's constructs) * Bottom-up / Top-down approaches to modelling context == Proposes a bottom-up internal model * Neural network with "switching" and "switched" edges: switching edges activate or disactivate switched edges. Thus a node can dis-activate the firing of edges, thus modelling context, i.e. some edges are transmitting only in certain context AU - Edmonds, Bruce PY - 1999/// UR - http://www.springerlink.com/content/cg26ep9m7ca500b4 ER -